• Published in Chemical Science of the Royal Society of Chemistry, successfully calculating the electron transfer process in the cryptochrome protein (ErCRY4) using quantum-classical hybrid computing. • Overcame the noise limits of current quantum hardware (NISQ) by introducing the VQE-PDFT method and a shallow-depth tailored quantum circuit (HEA). • Demonstrated that systematic errors caused by quantum noise are cancelled out when applying Marcus theory to calculate energy differences, presenting a practical algorithmic breakthrough. [Quantum Biology Society] Simulating strongly correlated electronic structure changes occurring inside complex biological proteins has been a daunting challenge, not only for conventional classical computers but also for current error-prone quantum computer (NISQ) devices. Recently, an innovative hybrid modeling technology combining only the strengths of quantum hardware and classical computers has been demonstrated, drawing attention from the academic community. A paper titled "Quantum-classical hybrid computation of electron transfer in a cryptochrome protein via VQE-PDFT and multiscale modeling" was published in the international journal Chemical Science, published by the Royal Society of Chemistry (RSC). A joint research team, including Dr. Jun-Han Huang of BGI Research and Professor Weitang Li of The Chinese University of Hong Kong, Shenzhen, presented a practical quantum algorithm for simulating biological electron transfer through this study. ■ The Meeting of Quantum and Classical, 'VQE-PDFT' Multiscale Architecture The research team targeted the electron transfer process between two tryptophan amino acids (TrpB, TrpC) within the cryptochrome (ErCRY4) of the European robin, known as the magnetic field-sensing protein of migratory birds. To compute this, they developed a hybrid framework called 'VQE-PDFT' (variational quantum eigensolver-pair-density functional theory). This approach is an efficient strategy that solves only the most complex multiconfigurational electronic correlations (static correlation) using the VQE algorithm on a quantum computer, while handling the remaining dynamic correlations with pair-density functional theory (PDFT) on a classical computer. Furthermore, they integrated multiscale QM/MM modeling, where the core region where the reaction occurs is calculated using quantum mechanics (QM), and the surrounding protein and solvent environment are treated with classical molecular mechanics (MM). ■ The Magic of Shallow-Depth Quantum Circuits and Error Cancellation To overcome the noise limitations of current quantum hardware, the researchers specially designed a shallow hardware-efficient ansatz (HEA) circuit with a depth of 4 to 6, optimized for the tryptophan structure, instead of the existing heavy quantum circuit (ROUCCSD) that reaches a depth of 35. In particular, the most groundbreaking discovery of this study emerged during the error analysis process. Although systematic errors occur in single-point energies calculated on noisy quantum devices, they demonstrated that these errors are dramatically cancelled out (error cancellation) during the process of calculating the energy difference between two states to obtain electron transfer parameters (reorganization energy and driving force) according to Marcus theory. ■ Successful Demonstration on a Superconducting Quantum Processor Thanks to this error cancellation effect, the electron transfer rate predictions obtained by the research team running the actual circuit on a 13-qubit superconducting quantum hardware matched very well with ideal noise-free simulator values and actual experimental ultrafast spectroscopy measurement results. This achievement is a practical milestone showing that the complex spin dynamics and electron transfer phenomena of biomolecules can be reliably calculated even with highly constrained quantum computers in the NISQ era. It is expected to provide a clear algorithmic breakthrough on how to utilize quantum technology in actual biological and chemical platforms, such as new drug discovery and massive enzyme complex analysis based on quantum computing in the future.
• Published in Chemical Science of the Royal Society of Chemistry, successfully calculating the electron transfer process in the cryptochrome protein (ErCRY4) using quantum-classical hybrid computing. • Overcame the noise limits of current quantum hardware (NISQ) by introducing the VQE-PDFT method and a shallow-depth tailored quantum circuit (HEA). • Demonstrated that systematic errors caused by quantum noise are cancelled out when applying Marcus theory to calculate energy differences, presenting a practical algorithmic breakthrough. [Quantum Biology Society] Simulating strongly correlated electronic structure changes occurring inside complex biological proteins has been a daunting challenge, not only for conventional classical computers but also for current error-prone quantum computer (NISQ) devices. Recently, an innovative hybrid modeling technology combining only the strengths of quantum hardware and classical computers has been demonstrated, drawing attention from the academic community. A paper titled "Quantum-classical hybrid computation of electron transfer in a cryptochrome protein via VQE-PDFT and multiscale modeling" was published in the international journal Chemical Science, published by the Royal Society of Chemistry (RSC). A joint research team, including Dr. Jun-Han Huang of BGI Research and Professor Weitang Li of The Chinese University of Hong Kong, Shenzhen, presented a practical quantum algorithm for simulating biological electron transfer through this study. ■ The Meeting of Quantum and Classical, 'VQE-PDFT' Multiscale Architecture The research team targeted the electron transfer process between two tryptophan amino acids (TrpB, TrpC) within the cryptochrome (ErCRY4) of the European robin, known as the magnetic field-sensing protein of migratory birds. To compute this, they developed a hybrid framework called 'VQE-PDFT' (variational quantum eigensolver-pair-density functional theory). This approach is an efficient strategy that solves only the most complex multiconfigurational electronic correlations (static correlation) using the VQE algorithm on a quantum computer, while handling the remaining dynamic correlations with pair-density functional theory (PDFT) on a classical computer. Furthermore, they integrated multiscale QM/MM modeling, where the core region where the reaction occurs is calculated using quantum mechanics (QM), and the surrounding protein and solvent environment are treated with classical molecular mechanics (MM). ■ The Magic of Shallow-Depth Quantum Circuits and Error Cancellation To overcome the noise limitations of current quantum hardware, the researchers specially designed a shallow hardware-efficient ansatz (HEA) circuit with a depth of 4 to 6, optimized for the tryptophan structure, instead of the existing heavy quantum circuit (ROUCCSD) that reaches a depth of 35. In particular, the most groundbreaking discovery of this study emerged during the error analysis process. Although systematic errors occur in single-point energies calculated on noisy quantum devices, they demonstrated that these errors are dramatically cancelled out (error cancellation) during the process of calculating the energy difference between two states to obtain electron transfer parameters (reorganization energy and driving force) according to Marcus theory. ■ Successful Demonstration on a Superconducting Quantum Processor Thanks to this error cancellation effect, the electron transfer rate predictions obtained by the research team running the actual circuit on a 13-qubit superconducting quantum hardware matched very well with ideal noise-free simulator values and actual experimental ultrafast spectroscopy measurement results. This achievement is a practical milestone showing that the complex spin dynamics and electron transfer phenomena of biomolecules can be reliably calculated even with highly constrained quantum computers in the NISQ era. It is expected to provide a clear algorithmic breakthrough on how to utilize quantum technology in actual biological and chemical platforms, such as new drug discovery and massive enzyme complex analysis based on quantum computing in the future.
a noiseless quantum circuit simulator, on the charge-transfer dataset confirmed that VQE-PDFT achieved results comparable to conventional MC-PDFT. Building upon this, we developed shallow-depth hardware-efficient ansatz circuits and integrated them into a QM/MM multiscale architecture to enable applications in complex biological systems. This extended framework, when applied to electron transfer in the European robin cryptochrome protein ErCRY4 with noiseless simulations, yielded transfer rates that aligned well with experimental measurements. Finally, as a proof-of-concept hardware demonstration, we executed reduced-density-matrix measurements for a single protein conformation on a 13-qubit superconducting device and showed the impact of noise through a comprehensive error analysis.
• A study published on arXiv presents a hybrid framework combining VQE, QM/MM, and statistical thermodynamics to predict protein-ligand binding free energies (BFEs) accurately and rapidly. • Explicitly handles polarization, charge redistribution, and electron correlation—often missed by purely classical methods—achieving an outstanding mean absolute error (MAE) of 1.10 kcal/mol. • Reduces computational costs 20-fold compared to conventional alchemical free energy perturbation (FEP) approaches (taking approximately 25 minutes per ligand), establishing a robust foundation for high-throughput lead optimization and future ML-integrated screening. [Quantum Biology Society] Predicting protein-ligand binding free energy (BFE), a core challenge in drug discovery, has long faced a dilemma: increasing accuracy requires massive amounts of computing resources. Traditional high-precision techniques, such as free energy perturbation (FEP), are computationally intensive and difficult to scale for large-scale applications. Recently, a groundbreaking study has been published that breaks through these limitations using a hybrid quantum-classical algorithm based on a variational quantum eigensolver (VQE), reducing computational costs by 20-fold and drawing intense interest from the pharmaceutical industry. ■ Perfect Synergy Between Quantum Mechanics and Statistical Thermodynamics The study titled "Synergistic Computational Approaches for Accelerated Drug Discovery: Integrating Quantum Mechanics, Statistical Thermodynamics, and Quantum Computing," released on the preprint repository arXiv, combines the advantages of classical and quantum computing. A research team led by Farzad Molani and Art E. Cho (from inCerebro and Korea University) designed a new framework that maximizes both computational efficiency and precision. The researchers integrated quantum mechanically refined ligand partial charges and QM/MM interaction evaluations based on the Mining Minima sampling technique. Notably, they added an electronic energy correction step using a variational quantum eigensolver (VQE), significantly boosting precision without compromising efficiency. ■ Overcoming the Blind Spots of Polarization and Electron Correlation The greatest strength of this hybrid model is its ability to explicitly handle effects such as polarization, charge redistribution, and electron correlation, which are frequently underestimated in purely classical scoring systems. By applying this system to 23 protein targets and 543 diverse ligands, the team successfully achieved an excellent MAE of 1.10 kcal/mol along with high fidelity in ranking predictions. ■ 20x Faster Computation: A Cornerstone for Machine Learning (ML) Integration The most encouraging achievement is the overwhelming computational speed. Using standard computing resources, the workflow completed calculations in approximately 25 minutes per ligand, representing a 20-fold reduction in costs compared to traditional FEP methods. The researchers emphasized that this level of accuracy and efficiency is highly suitable for the high-throughput lead optimization process in pharmaceutical drug discovery. Furthermore, this framework is expected to provide an excellent basis for realizing large-scale adaptive predictive screening strategies through seamless integration with future machine learning (ML) models.
• Published in AIP Publishing's APL Quantum, fully applying digital quantum simulation (DQS) to spin biology. • Successfully implemented the quantum spin dynamics of the radical pair mechanism utilizing the 'Trotterization' technique. • Analyzed the noise limits of actual quantum hardware and demonstrated the potential of utilizing quantum computing for the future development of new drugs targeting complex biomolecules. [Quantum Biology Society] In the emerging field of quantum biology, simulating spin-chemistry systems with actual biological complexity has posed computational limitations that are difficult to overcome with conventional classical computers. This is because as the number of elements constituting the spin system increases, the dimension of the Hilbert space required for computation grows exponentially. Recently, a study simulating biological phenomena using an actual digital quantum computer was published to resolve this problem, drawing the attention of the academic community. A paper titled "Simulating spin biology using a digital quantum computer: Prospects on a near-term quantum hardware emulator" was published on August 19 in APL Quantum, a journal of AIP Publishing. This research was led by a multinational joint research team including Pedro H. Alvarez and Clarice D. Aiello. ■ Mapping the Radical Pair Mechanism via Trotterization The radical pair mechanism (RPM), represented by the magnetic field sensing (magnetoreception) ability of migratory birds, centers on the quantum entanglement and coherence phenomena of electron spins in vivo. The research team introduced the Trotterization technique to directly apply gate-based digital quantum simulation (DQS) to biological systems. Through this technique, they successfully mapped the coherent spin dynamics of radical pairs onto quantum circuits. In ideal, noise-free quantum circuit simulations, they obtained accurate spin dynamics results consistent with the conventional classical Haberkorn master equation approach using only about 15 Trotter steps. ■ Analysis of Quantum Hardware Noise Limits and Future Prospects Furthermore, going beyond ideal environments, the research team conducted an in-depth analysis of the impact of realistic error models on simulations using a near-term (NISQ) quantum hardware emulator that mimics the noise profile of an IBM quantum device (e.g., ibmq_jakarta). The analysis revealed that realistic limitations were observed due to the constrained performance and noise (such as phase damping and amplitude damping) of current quantum hardware, including a shift in singlet yield and a decreased ability to sense magnetic field anisotropy. However, the researchers showed that by fitting an analytical model to the measured noisy data, the overall dynamical shape depending on the magnetic field angle could be successfully derived. In addition, they presented a practical upper limit for the number of Trotter steps that can be directly run on quantum devices if error mitigation techniques are introduced in the future. Unlike conventional classical computing methods that face exponential limits, the DQS approach possesses the potential to efficiently model biological spin systems simply through linear resource scaling by adding qubits. This study is evaluated as a groundbreaking first step demonstrating that digital quantum simulation can be utilized as an innovative tool for new drug development targeting complex biomolecules and ultra-precise biosensing modeling by directly calculating the entanglement and coherence of biological phenomena.
- A study published in the international journal npj Quantum Information (Nature Portfolio) presents a resource-efficient quantum algorithm framework based on the Variational Quantum Eigensolver (VQE) for peptide and protein folding. - By translating protein folding into a tetrahedral lattice model and combining Conditional Value-at-Risk (CVaR) VQE with a classical evolutionary algorithm, the number of required qubits was drastically reduced. - It successfully folded a 7-amino acid neuropeptide using only 9 qubits on actual IBM 20-qubit quantum hardware, demonstrating its practical potential for next-generation drug discovery in the NISQ (Noisy Intermediate-Scale Quantum) era. [Quantum Biology Society] The protein folding problem, which involves predicting the three-dimensional structure of proteins and peptides from their primary amino acid sequence, plays a central role in various applications such as chemistry, biology, and medicine. However, as the sequence lengthens, the number of possible structures to explore explodes exponentially, making it difficult for classical computers to handle its intrinsic NP-hard complexity. Recently, a new quantum algorithm that can efficiently solve this long-standing challenge even on noisy intermediate-scale quantum (NISQ) computers has been developed, drawing the attention of the academic community. The paper titled "Resource-efficient quantum algorithm for protein folding," published in the international journal npj Quantum Information, presents an innovative computing framework for the discovery of next-generation peptide drugs. The research team of Anton Robert and Ivano Tavernelli from IBM Quantum, IBM Research - Zurich in Switzerland successfully led this groundbreaking study. ■ The Meeting of Lattice Models and Qubit Optimization Until now, attempts to introduce quantum computing to protein folding have required an unattainably high number of qubits that current, near-term quantum hardware cannot handle. To solve this, the research team converted protein folding into a tetrahedral lattice model. This model can efficiently depict the growth of a polymer chain while maintaining chemically plausible bond angles. In particular, they adopted a method of adding penalty terms to the Hamiltonian to prevent unphysical amino acid overlaps (e.g., beads occupying the same lattice site). Through this, they succeeded in drastically reducing the number of qubits required for calculation while suppressing the complexity of the algorithm to the level of O(N⁴). ■ Demonstration of Folding on Actual Quantum Hardware This algorithm maximized efficiency by combining CVaR-VQE, a variation of the Variational Quantum Eigensolver (VQE), with a classical Differential Evolution optimizer. Applying this technique, the researchers simulated the folding of the 10-amino acid Angiotensin peptide using only 22 qubits, successfully finding the most stable energy state (native fold). Furthermore, they successfully demonstrated the folding of a 7-amino acid neuropeptide (APRLRFY) using only 9 qubits on IBM's 20-qubit quantum computer hardware. This study is a groundbreaking achievement proving that the protein folding problem, which previously required massive computing resources, can be successfully solved even in a noisy real quantum processor environment. It is expected to bring tremendous innovation to next-generation /de novo/ drug design and disease treatment research utilizing quantum computing in the future.
• Published on bioRxiv, successfully applied the variational quantum eigensolver (VQE) algorithm to calculate the electronic structure of a tau peptide fragment. • Overcame the exponential limits of existing classical computers, demonstrating the possibility of precise biomolecular energy calculations even on NISQ-era hardware. • Presents a scalable pathway for quantum chemical analysis of complex large protein systems through a fragment-based workflow. [Quantum Biology Society] Quantum computing technology, which overcomes the limitations of classical computers, has been deployed to elucidate the aggregation mechanism of the tau protein, one of the core causes of Alzheimer's disease. By precisely calculating the electronic structure of complex biological systems like peptides at the subatomic level, revolutionary changes are expected in the design of new drugs for treating neurodegenerative diseases in the future. Recently, an empirical study titled "Quantum Computing Reveals Energetics of Tau Peptide Fragments" was published on the preprint repository bioRxiv. A research team led by Dr. Anuj Guruacharya and Dr. Binita Rajbanshi at Nanil Therapeutics Inc. in the USA announced the results of successfully applying a quantum algorithm to a peptide system through this paper. ■ 'Variational Quantum Eigensolver (VQE)' Breaking Through Classical Limits Accurately predicting electron interactions within biomolecules is the core of new drug development, but there has been a fatal problem where the amount of computation required by classical computers increases exponentially as the molecule size grows. To solve this, the research team introduced the variational quantum eigensolver (VQE) algorithm, which can be run even on current noisy intermediate-scale quantum (NISQ) devices. The research team targeted a peptide fragment, which is a core region of the tau protein aggregates observed in the brains of Alzheimer's disease patients, and successfully derived its electronic structure and ground state energy under a quantum environment. ■ Elucidating the Differences Between Electronic Structure Methods and Classical Force Fields One of the greatest achievements of this study is that it rigorously compared and verified the energy calculations from the quantum algorithm against existing classical methods. As a result of the analysis, the quantum energy values from VQE showed systematic agreement with classical restricted Hartree-Fock (RHF) calculations, but exhibited a weak correlation with classical molecular dynamics (MD) force-field energies widely used in conventional drug design. This suggests that classical force-field models have limitations in fully capturing the electronic properties of biomolecules, proving that quantum mechanical treatment is essential for more precise drug design. ■ Securing Fragment-Based Scalability for Macromolecular Protein Analysis Instead of calculating the entire macromolecular protein at once, the researchers integrated a fragment-based workflow into VQE, dividing the molecule into smaller pieces for computation. This approach presents a practical pathway to scale to large systems using current quantum computing resources efficiently, while preserving the essential quantum characteristics of complex biological peptide systems. This empirical study is evaluated as a significant milestone demonstrating how quantum biology theory can be integrated into actual drug development platforms. Experts predict that as this technology advances, ultra-precise peptide drug design and the prediction of complex protein-protein interactions—which were impossible in the past due to computational cost issues—will become possible in near real-time.
Accurately capturing electron correlation in large-scale molecular systems remains one of the foremost challenges in quantum chemistry and a primary driver for the development of quantum algorithms. Classical configuration-interaction methods, while rigorous, suffer from exponential scaling, rendering them impractical for large or strongly correlated systems. Overcoming this limitation is central to realizing the promise of quantum computing in chemistry. Here, we investigate the pyridine-Li ion complex using three quantum algorithms: the variational quantum eigensolver (VQE), the subspace quantum diagonalization (SQD) method, and the recently introduced handover iterative VQE (HI-VQE). Our results demonstrate how new generations of hybrid quantum-classical frameworks overcome the scalability and noise sensitivity that constrain conventional VQE approaches. SQD and HI-VQE achieve ground-state energy calculations for problem sizes inaccessible to classical computation, marking a clear advance toward quantum advantage. In particular, HI-VQE enables calculations within active spaces as large as (24e,22o), requiring 44 qubits-well beyond the reach of classical CASCI and VQE. This capability provides a systematic pathway for incorporating increasing numbers of electrons into quantum treatment, thereby approaching exact molecular energies. Importantly, both SQD and HI-VQE exhibit robustness against hardware noise, a critical improvement over earlier approaches. By enabling quantum simulations of molecular systems previously deemed intractable, SQD and HI-VQE offer a realistic route toward practical quantum advantage in computational chemistry. The comparison between HI-VQE and SQD shows that optimizing circuit parameters is crucial for accurate simulation.